Predicting the environment from social media: A collective classification approach. (July 2020)

• Record Type: Journal Article
• Authors: Jeawak, Shelan S.; Jones, Christopher B.; Schockaert, Steven
• Is Part Of: Computers, environment and urban systems. Volume 82(2020)
• Publication Date: 2020-07
• Subjects: Collective prediction -- Text mining -- Social media -- Ecology -- Geographic information systems; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101487
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: We propose a method which uses Flickr tags to predict a wide variety of environmental features, such as climate data, land cover categories, species occurrence, and human assessments of scenicness. The role of Flickr tags in our method is two-fold. First, we show that Flickr tags capture information which is highly complementary to what is found in traditional structured environmental datasets. By combining Flickr tags with traditional datasets, we can thus make more accurate predictions than is possible using either Flickr tags or traditional datasets alone. Second, we propose a collective prediction model which crucially relies on Flickr tags to define a neighbourhood structure. The use of a collective prediction formulation is motivated by the fact that most environmental features are strongly spatially autocorrelated. While this suggests that geographic distance should play a key role in determining neighbourhoods, we show that considerable gains can be made by additionally taking Flickr tags and traditional data into consideration. Highlights: We use Flickr as a supplementary source of ecological information. We propose a collective prediction model which crucially relies on Flickr tags to define a neighbourhood structure. It can be used to predict a wide variety of environmental features, such as species occurrence, land cover classes and scenicness.